Report with Evidently

%load_ext kedro.ipython

[09/05/24 21:27:53] INFO     Using                                                                  __init__.py:249
                             'c:\Users\Admin\miniconda3\envs\recsys\Lib\site-packages\kedro\framewo                
                             rk\project\rich_logging.yml' as logging configuration.                                

[09/05/24 21:27:54] INFO     Registered line magic '%reload_kedro'                                   __init__.py:58

                    INFO     Registered line magic '%load_node'                                      __init__.py:60

                    INFO     Resolved project path as:                                              __init__.py:175
                             c:\Users\Admin\Desktop\Semester_7\MLOps\AI-839\srinivasan-ai-839.                     
                             To set a different path, run '%reload_kedro <project_root>'                           

[09/05/24 21:27:55] WARNING  c:\Users\Admin\miniconda3\envs\recsys\Lib\site-packages\kedro_viz\__in warnings.py:112
                             it__.py:13: KedroVizPythonVersionWarning: Please be advised that Kedro                
                             Viz is not yet fully                                                                  
                                     compatible with the Python version you are currently using.                   
                               warnings.warn(                                                                      
                                                                                                                   

                    INFO     Kedro is sending anonymous usage data with the sole purpose of improving plugin.py:233
                             the product. No personal data or IP addresses are stored on our side. If              
                             you want to opt out, set the `KEDRO_DISABLE_TELEMETRY` or `DO_NOT_TRACK`              
                             environment variables, or create a `.telemetry` file in the current                   
                             working directory with the contents `consent: false`. Read more at                    
                             https://docs.kedro.org/en/stable/configuration/telemetry.html                         

[09/05/24 21:27:56] INFO     Kedro project Srinivasan-ai-839                                        __init__.py:141

                    INFO     Defined global variable 'context', 'session', 'catalog' and            __init__.py:142
                             'pipelines'                                                                           

[09/05/24 21:27:57] INFO     Registered line magic 'run_viz'                                        __init__.py:148

dataset = catalog.load("dataset_id_214")

[09/05/24 21:49:58] INFO     Loading data from dataset_id_214 (CSVDataset)...                   data_catalog.py:539

import pandas as pd
import numpy as np

from sklearn.datasets import fetch_california_housing

from evidently import ColumnMapping

from evidently.report import Report
from evidently.metrics.base_metric import generate_column_metrics
from evidently.metric_preset import (
    DataDriftPreset,
    TargetDriftPreset,
    DataQualityPreset,
    RegressionPreset,
)
from evidently.metrics import *

from evidently.test_suite import TestSuite
from evidently.tests.base_test import generate_column_tests
from evidently.test_preset import (
    DataStabilityTestPreset,
    NoTargetPerformanceTestPreset,
    RegressionTestPreset,
)
from evidently.tests import *

import warnings

warnings.filterwarnings("ignore")
warnings.simplefilter("ignore")

# noise_probability = 0.1  # Set the probability of flipping (10% noise)

# # Generate random values between 0 and 1, and flip based on the noise_probability
# flip_mask = np.random.rand(dataset.shape[0]) < noise_probability

# # Flip the boolean values wherever the flip_mask is True
# dataset['y'] = np.where(flip_mask, ~dataset['y'], dataset['y'])
# # dataset['y'] = dataset['y'] + np.random.normal(0,1,dataset.shape[0])

reference = dataset.sample(n=50, replace=False)
current = dataset.sample(n=50, replace=True)

# dataset.head()
reference.head()

	checking_status	duration	credit_history	purpose	credit_amount	savings_status	employment	installment_commitment	personal_status	other_parties	...	X_5	X_6	X_7	X_8	X_9	X_10	X_11	X_12	X_13	y
2	no checking	24	critical/other existing credit	radio/tv	5103.0	<100	<1	3	male mar/wid	none	...	0.135713	0.185599	0.992303	0.778875	0.236489	0.317112	0.751187	0.080140	0.272325	True
53	>=200	18	existing paid	furniture/equipment	3049.0	<100	<1	1	female div/dep/mar	none	...	0.847548	0.809026	0.235335	0.986156	0.187755	0.652575	0.937840	0.649408	0.557902	True
92	>=200	36	existing paid	radio/tv	4473.0	<100	>=7	4	male single	none	...	0.314592	0.653284	0.703138	0.151543	0.624606	0.771431	0.813860	0.958305	0.681945	True
81	no checking	36	critical/other existing credit	furniture/equipment	7127.0	<100	<1	2	female div/dep/mar	none	...	0.755093	0.773156	0.326619	0.680981	0.180544	0.342531	0.790025	0.660239	0.328569	False
27	0<=X<200	15	existing paid	repairs	1308.0	<100	>=7	4	male single	none	...	0.321287	0.562743	0.384324	0.404911	0.697955	0.207398	0.259863	0.708330	0.015333	True

5 rows × 35 columns

report = Report(metrics=[DataDriftPreset()])
report.run(reference_data=reference, current_data=current)
report